A Hyperheuristic for the Dial-a-Ride Problem with Time Windows

Abstract

The dial-a-ride problem with time windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because of the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed, and evaluated: hyperheuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyperheuristic uses simple and easy-to-implement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.

Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

“…Additionally, as in the OPDPSTRP, each vehicle starts at its location (regarded as a depot) and ends at the final delivery point of the contents transported by the vehicle, so it can be considered as a multi-depot (vehicles) problem. Most OPDP research is based on single depot, such as that reviewed by Psaraftis [12], [13] (1983), Desrosiers et al [31], Lin et al [29], Li et al [32], Letchford et al [24], Urra et al [33], Muelas et al [28], Qiu et al [34] and Li et al [25]. There is also some OPDP research based on multiple depots (vehicles), which is mainly concerned with the Taxi-sharing Problem and Ride-sharing Problem.…”

hMod: A software framework for assembling highly detailed heuristics algorithms

“…Although the implementation in Figure 10 looks basic, in previous research, hMod has been used for implementing more complex heuristic architectures, particularly hyperheuristics for solving combinatorial problems such as DARPTW itself or the multidimensional KP (MKP). 12,13 Framework features allowed the implementation of high-level mechanisms and the integration of them with additional tools, such as automatic parameter configuration systems. Figure 11 presents a detailed example of the hyperheuristic architecture implemented with hMod and applied to solve the MKP problem.…”

“…hMod has been mentioned in previous works related to the optimization field, in which the framework was successfully used for the design and implementation of complex algorithmic architectures, such as hyperheuristics. 12,13 In contrast, this work contributes by deepening hMod features and AA capabilities.…”

“…Additionally, the framework is bundled with features for integrating algorithmic components with a modular and automated approach using dependency injection techniques. hMod has been mentioned in previous works related to the optimization field, in which the framework was successfully used for the design and implementation of complex algorithmic architectures, such as hyperheuristics . In contrast, this work contributes by deepening hMod features and AA capabilities.…”

Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

“…As the time went, HH has been successively applied to various combinational optimization problems, such as educational timetabling problems (Kendall and Hussin, Burke et al) [53,54], VRP (Walker et al) [55], construction levelling problems (Koulinas and Anagnostopoulos) [56], variability test of feature models (Strickler et al) [57], t-ways test suite generation (Zamli et al) [51,58], dial-a-ride problem (Urra et al) [59], and other issues. We refer the interested readers to the papers of Chakhlevitch and Cowling [60] and Burke et al [46,61] for extensive review about hyperheuristic.…”